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Autor Tópico: Efficient Machine Learning  (Lida 414 vezes)

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Efficient Machine Learning
« em: 29 de Setembro de 2019, 09:11 »

Efficient Machine Learning
.MP4 | Video: 1280x720, 30 fps(r) | Audio: AAC, 44100 Hz, 2ch | 991 MB
Duration: 2.5 hours | Genre: eLearning | Language: English
Become an Advanced Machine Learning Specialist, Learn Preprocessing, Feature Engineering, Model Evaluation and Selection.

What you'll learn

    Master Machine Learning
    Performing Ideal Preprocessing
    Understand Feature Engineering
    Understand Feature Selection
    Know the Best Way to Evaluate Models
    Analyse Models and Overcome its Challenges
    Hyperparameters Tuning
    Make Accurate Predictions
    Work with Real-World Data

Requirements

    Basic Understanding of Machine Learning
    Some Programming Experience

Description

If you're a machine learning specialist looking to make the transaction into the real-world AI applications.

This comprehensive course will be your guide to learning how to scale-up your machine learning model to the optimal state possible, you 'll be learning everything you need to move you machine learning model to the next stage.

This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

You'll learn the machine learning, AI, and data mining techniques real employers are looking for, including:

Handling Missing Values

Label Encoder

One-Hot Encoder

Normalization

Standardization

Binarization

Principal Analysis Component (PCA)

Manual Feature Engineering

Automatic Feature Engineering

Feature Selection

Model Evaluation

Confusion Matrix

Precision and Recall

F1-score and Fbeta-score

Area Under Curve (AUC)

Overfitting vs Underfitting

Cross-Validation

Analyzing Learning Curves

Hyperparameters Tuning

Who this course is for:

    Anyone interested in Machine Learning
    Any data analysts who want to level up in Machine Learning
    Anyone who want to master Machine Learning


               

Download link:
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